AIMC Topic: Middle Aged

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Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain.

Scientific reports
Pain is common in middle-aged and older adults, has also been identified as a fall risk factor, whereas the mechanism of falls in pain is unclear. This study included 13,074 middle-aged and older adults from the China health and retirement longitudin...

Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer.

Scientific reports
Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patients' quality of life. This study aimed to establish a combined model of radiomics, dosiomics, deep learning (DL) based on simulated location CT and dos...

Deep learning progressive distill for predicting clinical response to conversion therapy from preoperative CT images of advanced gastric cancer patients.

Scientific reports
Identifying patients suitable for conversion therapy through early non-invasive screening is crucial for tailoring treatment in advanced gastric cancer (AGC). This study aimed to develop and validate a deep learning method, utilizing preoperative com...

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data.

BMJ open
OBJECTIVES: In lung transplantation (LTx), a priority is assigned to each candidate on the waiting list. Our primary objective was to identify the key factors that influence the allocation of priorities in LTx using machine learning (ML) techniques t...

MixOmics Integration of Biological Datasets Identifies Highly Correlated Variables of COVID-19 Severity.

International journal of molecular sciences
Despite several years passing since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity of COVID-19 disease and complications of SARS-CoV-2 infection. While many large-scale multi-omic data...

Accelerated Biological Aging in Exfoliation Glaucoma Assessed by Fundus-Derived Predicted Age and Advanced Glycation End Products.

International journal of molecular sciences
Glaucoma is an age-related neurodegenerative disease characterized by progressive optic nerve damage. Accelerated biological aging, assessed using predicted age derived from fundus images, may serve as a biomarker for glaucoma progression. This study...

Assessment of Elapsed Time Between Dental Radiographs Using Siamese Network.

Studies in health technology and informatics
Recently, machine learning methods have emerged to predict dental disease progression, often relying on costly annotated datasets and frequently exhibiting low generalization performance. This study evaluates the application of Siamese networks for d...

Machine Learning and Urinary Incontinence in Prostate Cancer: A Generalized Additive Model of Physical Activity and Recovery Patterns.

Studies in health technology and informatics
The ASCAPE project aims to improve the health-related quality of life of prostate cancer patients using artificial intelligence-driven solutions. This study tries to unravel the complex relationships between patient data variables and urinary inconti...

Processing UK Biobank High Resolution Accelerometry Data for Unsupervised Identification of Activity Profiles and Their Differences in Clinically Relevant Outcome Parameters - The ATLAS Index Revisited.

Studies in health technology and informatics
Accelerometer data obtained with wearable devices over extended periods of time provides objective, valuable information on activity behavior. Building on previous work to derive easy-to-interpret activity parameters - the Activity Types from Long-te...

Perceptions of the German General Population Towards Implementing Artificial Intelligence in Medical Care: A Population-Based Survey.

Studies in health technology and informatics
The rise of artificial intelligence (AI) in medical care presents several opportunities, including improving patient outcomes. As part of the PEAK project (Perspectives on the Use and Acceptance of Artificial Intelligence in Medical Care), this study...